#region License Information
/* HeuristicLab
 * Copyright (C) 2002-2016 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
 *
 * This file is part of HeuristicLab.
 *
 * HeuristicLab is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 *
 * HeuristicLab is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 *
 * You should have received a copy of the GNU General Public License
 * along with HeuristicLab. If not, see <http://www.gnu.org/licenses/>.
 */
#endregion

using System;
using System.Collections.Generic;

namespace HeuristicLab.Analysis.FitnessLandscape {
  public static class RuggednessCalculator {
    /// <summary>
    /// Calculates statistical correlation length as defined by Hordijk, W., 1996. A measure of landscapes. Evolutionary computation, 4(4), pp.335-360.
    /// </summary>
    /// <param name="qualities">The quality trail observed.</param>
    /// <param name="acf">The autocorrelation values for each step s, including 0 => acf[0] = 1.</param>
    /// <param name="limit">The statistical limit, correlation length will be the last step before acf falls within this limit. If omitted it is calculated as 2 / sqrt(qualities.Length).</param>
    /// <returns>The statistical correlation length</returns>
    public static int CalculateCorrelationLength(double[] qualities, out double[] acf, double? limit = null) {
      if (!limit.HasValue) limit = 2.0 / Math.Sqrt(qualities.Length);
      double[] correlations = new double[qualities.Length];
      alglib.corr.corrr1dcircular(qualities, qualities.Length, qualities, qualities.Length, ref correlations);
      double mean = 0;
      double variance = 0;
      double skewness = 0;
      double kurtosis = 0;
      alglib.basestat.samplemoments(qualities, qualities.Length, ref mean, ref variance, ref skewness, ref kurtosis);
      List<double> autocorrelation = new List<double>() { 1.0 };
      int correlationLength = -1, counter = 1;
      for (; counter < qualities.Length / 2; counter++) {
        double value = correlations[counter] / qualities.Length - mean * mean;
        if (variance > 0)
          value = Math.Max(Math.Min(value / variance, 1.0), -1.0);
        else
          value = 1;
        autocorrelation.Add(value);
        if (Math.Abs(value) < limit && correlationLength < 0) correlationLength = counter;
      }
      acf = autocorrelation.ToArray();
      return correlationLength - 1;
    }
  }
}